[ET-VK][testing] Q/DQ/CQP op comprehensive delegate dynamic quantization testing

backends/vulkan/test/op_tests/dequantize_test.cpp

@@ -744,49 +744,6 @@ void test_vulkan_dequantize_per_tensor_tensor(
       vkcompute::utils::kTexture3D);
 }
 
-// Wrapper function to test both buffer and texture storage types
-void test_vulkan_dequantize_per_channel(
-    const std::vector<int>& input_sizes,
-    const std::vector<float>& scales,
-    const std::vector<int>& zero_points,
-    int64_t axis,
-    int64_t quant_min,
-    int64_t quant_max,
-    at::ScalarType dtype,
-    at::ScalarType out_dtype) {
-  // Test with buffer storage
-  test_vulkan_dequantize_per_channel_impl(
-      input_sizes,
-      scales,
-      zero_points,
-      axis,
-      quant_min,
-      quant_max,
-      dtype,
-      out_dtype,
-      vkcompute::utils::kBuffer,
-      vkcompute::utils::kBuffer);
-
-  // Telling the system to expect a float instead of a double
-  // since the shader can only return 32bit anyways
-  if (out_dtype == at::kDouble) {
-    out_dtype = at::kFloat;
-  }
-
-  // Test with texture storage
-  test_vulkan_dequantize_per_channel_impl(
-      input_sizes,
-      scales,
-      zero_points,
-      axis,
-      quant_min,
-      quant_max,
-      dtype,
-      out_dtype,
-      vkcompute::utils::kTexture3D,
-      vkcompute::utils::kTexture3D);
-}
-
 void test_reference_dequantize_per_tensor(
     const std::vector<int>& input_sizes,
     float scale,

backends/vulkan/test/test_vulkan_delegate.py

@@ -1964,3 +1964,99 @@ def forward(self, x):
                     GroupNormModule(num_groups, num_channels),
                     sample_inputs,
                 )
+
+    def test_vulkan_backend_full_quantization_workflow(self):
+        class FullQuantizationWorkflowModule(torch.nn.Module):
+            def __init__(self):
+                super().__init__()
+
+            def forward(self, x):
+                # Step 1: Choose quantization parameters per tensor
+                scale, zero_point = (
+                    torch.ops.quantized_decomposed.choose_qparams.tensor(
+                        x,
+                        quant_min=-2147483648,  # int32 min
+                        quant_max=2147483647,  # int32 max
+                        eps=1e-5,
+                        dtype=torch.int32,
+                    )
+                )
+
+                # Step 2: Quantize using the calculated parameters
+                quantized = torch.ops.quantized_decomposed.quantize_per_tensor.tensor(
+                    x,
+                    scale,
+                    zero_point,
+                    quant_min=-2147483648,  # int32 min
+                    quant_max=2147483647,  # int32 max
+                    dtype=torch.int32,
+                )
+
+                # Step 3: Dequantize back to float
+                dequantized = (
+                    torch.ops.quantized_decomposed.dequantize_per_tensor.tensor(
+                        quantized,
+                        scale,
+                        zero_point,
+                        quant_min=-2147483648,  # int32 min
+                        quant_max=2147483647,  # int32 max
+                        dtype=torch.int32,
+                    )
+                )
+
+                return dequantized
+
+        full_workflow_module = FullQuantizationWorkflowModule()
+        sample_inputs = (torch.rand(size=(2, 3, 4), dtype=torch.float32),)
+
+        # Use higher tolerance since quantization introduces some error
+        self.lower_module_and_test_output(
+            full_workflow_module, sample_inputs, atol=5e-3, rtol=5e-3
+        )
+
+    def test_vulkan_backend_full_per_token_quantization_workflow(self):
+        class FullPerTokenQuantizationWorkflowModule(torch.nn.Module):
+            def __init__(self):
+                super().__init__()
+
+            def forward(self, x):
+                # Step 1: Choose quantization parameters per token
+                scale, zero_point = (
+                    torch.ops.quantized_decomposed.choose_qparams_per_token_asymmetric.default(
+                        x,
+                        dtype=torch.int32,
+                    )
+                )
+
+                # Step 2: Quantize using the calculated parameters per token
+                quantized = torch.ops.quantized_decomposed.quantize_per_token.default(
+                    x,
+                    scale,
+                    zero_point,
+                    quant_min=-2147483648,  # int32 min
+                    quant_max=2147483647,  # int32 max
+                    dtype=torch.int32,
+                )
+
+                # Step 3: Dequantize back to float per token
+                dequantized = (
+                    torch.ops.quantized_decomposed.dequantize_per_token.default(
+                        quantized,
+                        scale,
+                        zero_point,
+                        quant_min=-2147483648,  # int32 min
+                        quant_max=2147483647,  # int32 max
+                        dtype=torch.int32,
+                        output_dtype=torch.float32,
+                    )
+                )
+
+                return dequantized
+
+        full_per_token_workflow_module = FullPerTokenQuantizationWorkflowModule()
+        sample_inputs = (torch.rand(size=(6, 4), dtype=torch.float32),)
+
+        # Use higher tolerance since quantization introduces some error
+        self.lower_module_and_test_output(
+            full_per_token_workflow_module, sample_inputs, atol=5e-3, rtol=5e-3
+        )